That's a great question. I never worked on any cool NASA stuff which would involve large scale number crunching. In the corpo space, that's not been my experience at all. We were trying to solve big data problems of like, how to report on medical claims that are in flight (which are hardly ever static until much later after the claim is long completed and no longer interesting to anyone) and do it at scale of tens of thousands per hour. It never went that well, tbh, because it is so hard to validate what a "claim" even is since it is changing in real time. I don't think excess GPUs would help with that.
lot's of columns are float valued, GPU tensor cores can be programmed to do many operations between different float/int valued vectors. Strings can also be processed in this manner as they are simply vectors of integers. NVidia publishes official TPC benchmarks for each GPU release.
The idea of a GPU database has been reasonably well explored, they are extremely fast - but have been cost ineffective due to GPU costs. When the dataset is larger than GPU memory, you also incur slowdowns due to cycling between CPU and GPU memory.